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We present Bayesian models for finding the longitudinal causal effects of a randomized two-arm training program when compliance with the randomized assignment is less than perfect in the training arm (but perfect in the non-training arm) for reasons that are potentially correlated with the outcomes. We deal with the latter confounding problem under the principal stratification framework of Sommer and Zeger (1991) and Frangakis and Rubin (1999), and others. Building on the Bayesian contributions of Imbens and Rubin (1997), Hirano et al. (2000), Yau and Little (2001) and in particular Chib (2007) and Chib and Jacobi (2007, 2008), we construct rich models of the potential outcome sequences (with and without random effects), show how informative priors can be reasonably formulated, and present tuned computational approaches for summarizing the posterior distribution. We also discuss the computation of the marginal likelihood for comparing various versions of our models. We find the causal effects of the observed intake from the predictive distribution of each potential outcome for compliers. These are calculated from the output of our estimation procedures. We illustrate the techniques and ideas with data from the 1994 JOBS II trial that was set up to test the efficacy of a job training program on subsequent mental health outcomes.

With growing concerns about an academic literacy crisis plaguing the education system in South Africa, tertiary institutions have to find ways to strengthen the academic literacy skills of underprepared students transitioning into higher education. This is more pressing for low socioeconomic status students who are linguistically marginalised and face historically poor graduation prospects. In response, this chapter offers a snapshot of two studies conducted in South Africa that sought to test the efficacy of a purposefully designed academic literacy intervention (Reading to Learn (RtL)). The intervention sought to address inequitable academic literacy skills development of linguistically marginalised students, who are also socioeconomically disadvantaged. Two small-scale, longitudinal studies were run in two separate educational contexts in South Africa – a senior secondary school context and a tertiary context with largely first-generation undergraduate students. Results of both studies showed the RtL intervention to be successful at raising the level of academic writing skills of the research participants. Furthermore, similar to other RtL studies conducted globally, the two studies found weaker-performing students made the greatest gains in their academic writing skills, showing evidence of a convergence effect – more equitable learning outcomes being exhibited in the English classroom.

The aim of this chapter is to review and illustrate a step-by-step guideline in conducting fuzzy-set Qualitative Comparative Analysis (fsQCA) in tourism and hospitality studies. As an emerging method, fsQCA is simultaneously quantitative and qualitative in nature which makes it an appropriate method for social science disciplines including tourism and hospitality area because of complex nature of relationships between multiple variables where theories and models are underdeveloped. Unlike conventional statistical techniques, fsQCA is an asymmetrical analysis technique that provides a holistic view and interrelationships among several conditions using Boolean algebra. The fsQCA analyses produce comprehensive assessment by revealing causal combinations of antecedents to predict an outcome; and identify sufficient configurations (i.e., causal combinations and recipes) and necessary condition/s. By utilizing this method, researchers would be able to produce complex, comprehensive, and robust results.

Since the seminal works by Granger and Joyeux (1980) and Hosking (1981), estimations of long-memory time series models have been receiving considerable attention and a number of parameter estimation procedures have been proposed. This paper gives an overview of this plethora of methodologies with special focus on likelihood-based techniques. Broadly speaking, likelihood-based techniques can be classified into the following categories: the exact maximum likelihood (ML) estimation (Sowell, 1992; Dahlhaus, 1989), ML estimates based on autoregressive approximations (Granger & Joyeux, 1980; Li & McLeod, 1986), Whittle estimates (Fox & Taqqu, 1986; Giraitis & Surgailis, 1990), Whittle estimates with autoregressive truncation (Beran, 1994a), approximate estimates based on the Durbin–Levinson algorithm (Haslett & Raftery, 1989), state-space-based maximum likelihood estimates for ARFIMA models (Chan & Palma, 1998), and estimation of stochastic volatility models (Ghysels, Harvey, & Renault, 1996; Breidt, Crato, & de Lima, 1998; Chan & Petris, 2000) among others. Given the diversified applications of these techniques in different areas, this review aims at providing a succinct survey of these methodologies as well as an overview of important related problems such as the ML estimation with missing data (Palma & Chan, 1997), influence of subsets of observations on estimates and the estimation of seasonal long-memory models (Palma & Chan, 2005). Performances and asymptotic properties of these techniques are compared and examined. Inter-connections and finite sample performances among these procedures are studied. Finally, applications to financial time series of these methodologies are discussed.

This chapter investigates the structure of a simple vertical competition model by exhibiting the competitive links among assemblers, component producers, and integrated firms in a generalization of Cournot's model of the vertical integration by allowing any degree of competition among cohorts. Vertical integration in the model can take place by vertical mergers, forward or backward integration. Vertical integration is highly profitable and always reduces the price of the final product. The cost-raising strategy of an integrated firm buying out unneeded component producers is profitable and detrimental to consumers only if the firm faces no competition from other integrated firms.

The changes in women’s and men’s work lives have been considerable in recent decades. Yet much of the recent research on gender differences in employment and earnings has been of a more snapshot nature rather than taking a longer comparative look at evolving patterns. In this paper, we use 50 years (1964–2013) of US Census Annual Demographic Files (March Current Population Survey) to track the changing returns to human capital (measured as both educational attainment and potential work experience), estimating comparable earnings equations by gender at each point in time. We consider the effects of sample selection over time for both women and men and show the rising effect of selection for women in recent years. Returns to education diverge for women and men over this period in the selection-adjusted results but converge in the OLS results, while returns to potential experience converge in both sets of results. We also create annual calculations of synthetic lifetime labor force participation, hours, and earnings that indicate convergence by gender in worklife patterns, but less convergence in recent years in lifetime earnings. Thus, while some convergence has indeed occurred, the underlying mechanisms causing convergence differ for women and men, reflecting continued fundamental differences in women’s and men’s life experiences.